The Global Environmental Benefits of Halving Avoidable Consumer Food Waste

Avoidable consumer food waste (ACFW) is a global environmental issue wasting key resources and causing emissions, especially in high food-producing nations. We trace ACFW to its origin to assess emissions, water use, and land use. We show that ACFW impacts are dominated by commodities like beef, dairy, rice, and wheat. Over 80% of impacts are domestic, but impacts embodied in trade affect a few major food-producing countries under environmental pressure. A 50% reduction in ACFW could save up to 198 Mt CO2eq in emissions, 30 Gm3 of blue water, and 99 Mha of land. Targeting key commodities in impactful countries (e.g., US beef waste) could achieve significant benefits. Sparing wasted land and returning it to its potential natural vegetation could sequester 26 Gt CO2eq long-term (17–35 Gt CO2eq). Finally, while the 50% ACFW reduction lines up with Sustainable Development Goal (SDG) 12.3b for the avoidable portion of food waste, a total of 276 Mt of unavoidable consumer food waste is also generated, which cannot be readily reduced. Achieving a 50% reduction in total food waste would require a 93% reduction in ACFW. Tracking the spatial impacts of ACFW can elucidate the concrete benefits of policies aiming at SDG 12.3b.


Blue water impacts
Blue water impacts: commodity contribution.
A total of ~60 Gm 3 was wasted due to consumer avoidable food waste.This is equivalent to the water use of Brazil, the 13 th largest consumer of water.

Greenhouse gases impacts
Greenhouse gases impacts: commodity contribution.
A total of ~396 MtCO 2 eq was wasted due to consumer avoidable food waste.This is equivalent to the greenhouse gases emissions of France in 2010.seeds, feed, etc.), and exports.Consequently, the FBSs offer an average measure of food supply at the national level for each country.This measurement is presented in kilograms per capita per annum, encompassing around 90 subtypes of food products or 18 aggregated categories of food 2 .Notably, FAOSTAT reports the availability of food in primary-equivalent terms.Consequently, for processed items, the compiled quantities are expressed in primaryequivalent, which might appear greater than the practical amounts found at the retail level.For instance, products like pasta or bread are quantified as their wheat-equivalent 3 .To accurately assess the actual quantities of available food, the FBSs need to be adjusted using technical conversion factors (TCFs) in line with the methodology outlined by Vanham et al 4 .This adjustment is crucial for food waste calculations.In this study, we employ the TCFs initially developed by FAO 4 and compiled by Bruckner et al 5 .The TCFs supplied by FAO are tailored to the most finely detailed food items recorded in their data.The TCFs are therefore used to calculate the product-equivalent from the primary-equivalent data of the FBSs (Eq.1).

Harvested land
=    *   Eq. 1 Where:   is the corrected, actual quantity of a food item  available at the Distribution stage, in kg    is the primary-equivalent quantity of food item , compiled in the FBS, in kg   is the Technical Conversion Factor of food item , as a percentage.This step yields the actual amount of food available at the retail-level.Prior to reaching their ultimate consumption point, which includes both food services and households, food products might also be wasted during the Distribution stage.This wastage essentially reduces the quantity of food that effectively makes it to the Consumption stage.Consequently, the subsequent step involves accounting for these losses that occur before consumption.
To calculate the losses during the Distribution stage, it's important to consider the nature of the food products-whether they are processed or fresh-since they exhibit varying rates of food waste incidence 6 .Consequently, FAO provides distinct estimates for food waste, depending on whether a food type is consumed in a processed or fresh state.This differentiation concerning the nature of products applies to the following grouped food categories: Vegetables, Fruits, Starchy Roots, and Fish and Seafood, with estimates available for various regions across the globe.These regional waste estimates are detailed in the comprehensive FAO Global Food Estimates report.This report presents the incidence of food waste for different world regions during the Distribution stage (retail level) for diverse food groups, considering whether they are processed or fresh.Aligning the more aggregated food categories used in the Global Food Estimates with the 18 consolidated food groups in the FBSs requires a harmonization of food item classifications.This harmonization is applied to the 90 disaggregated food items.
The actual quantities of food that ultimately reach households and food services in each country are then calculated.This involves first determining the overall amount of both processed and fresh food that is wasted during the Distribution stage (using Equations 2 and 2.bis).These calculated losses are then subtracted from the total to obtain the actual quantities of food that consumers ultimately receive (as described in Equation 3).The losses are quantified for both Fresh and Processed products for the relevant food types.

Eq. 2
Where: -   is the quantity of a food item , consumed fresh, wasted at the Distribution stage, in kg.-   is the consumption type share of food item  considered to be consumed fresh (without any processing), as a percentage.-   is the Distribution food waste factor from    to which food item  belong and applied to food item .

Eq. 2.bis
Where: -   is the quantity of a food item , consumed fresh, wasted at the Distribution stage, in kg -   is the consumption type share of food item  considered to be consumed processed, as a percentage.-   is the Distribution food waste factor from   , considered to be processed, to which food item  belong and applied to food item .
The amounts of food available to consumers can then be derived with the following equations.

Eq. 3 and 3 bis
Where: -   is the quantity of a food item , fresh, available at the Consumption stage (food services and households), in kg -  is the corrected, actual quantity of a food item  available at the Distribution stage, in kg -  is the consumption type share of food item  considered to be consumed, whether fresh or processed, as a percentage.-  is the quantity of a food item , consumed fresh or processed, wasted at the Distribution stage, in kg.

Quantifying Avoidable Consumer Food Waste
Avoidable Consumer Food Waste (ACFW) is calculated by combining FAO Global Food Estimates at the consumption stage for every country, with their respective regional estimates.The food waste estimates take into consideration both ACFW and Unavoidable Consumer Food Waste (UCFW) (e.g., sheel, peels, seed).To accurately calculate ACFW, the UCFW fraction must be subtracted from the total calculated food waste at consumption stage.
Quantifying the total UCFW is achieved through the "waste floor" approach.The "waste floor" approach aims to quantify the minimum volume of UCFW generated by each country.It calculates the total "minimal" volume of UCFW associated with the final consumption of food within households and food services 7 .To accomplish this, we employ data from De Laurentiis et al. 7 for vegetables, fruits, and starchy roots.Additionally, WRAP 8 provides estimates for the meat group and its subcategories (bovine, pork, poultry, sheep), as well as fish and seafood.Furthermore, it's assumed that stimulants like coffee and tea grounds constitute 100% of UCFW.Inedible fraction estimates for eggshells 9 are also incorporated.A core assumption in this approach is to consider that processed food products (such as cans, jars, frozen, juice, dried) will be entirely edible, and therefore will not generate any UCFW (Eq.4bis), as they had the inedible portions removed at the processing stage.It is also coherent with the "waste floor" approach seeking to quantify the minimal amounts of UFW.As a result, the inedible fractions of the relevant food products are matched with their respective food groups.The cumulative volume of UFW for each country is then quantified by multiplying the fraction with the total available quantities of (fresh) food products post-distribution (as described in Eq.4).

Eq. 4
Where: -  is the unavoidable food waste quantity of a food item , consumed fresh, that is generated at the Consumption stage, in kg -   is the quantity of a food item , fresh, available at the Consumption stage (food services and households), in kg -   is the inedible fraction of food item , consumed fresh, as a percentage.
=  Eq. 4.bis Where: -   is considered to be 0 as the processed food item  is considered to have been stripped of the inedible, or unavoidable waste elements.
This step results in the amounts of UCFW for each country.
To obtain the final amounts of ACFW, the inedible shares of food products must also be considered, and subtracted from the total amounts of food waste (ACFW+UCFW) that are themselves calculated via the FAO Global Food Estimates (Eq.5).

Eq. 5
Where: -   is the quantity of a food item , consumed fresh, wasted at the Consumption stage, in kg.-   is the quantity of a food item , fresh, available at the Consumption stage (food services and households), in kg -   is the Consumption food waste factor from    to which food item  belong, and applied to food item .
=    * ( -   ) Eq. 6 Where: -   is the edible quantity of a food item , consumed fresh, that is wasted at the Consumption stage, in kg -   is the quantity of a food item , consumed fresh, wasted at the Consumption stage, in kg.-   is the inedible fraction of food item , consumed fresh, as a percentage.
For processed food item , as    = 0, the following relationship can be derived: Eq. 7 This step yields the total amounts of ACFW for each food type for every country compiled in the FBSs.It should be noted that some limitations exist around the food waste factors used in this model.Food waste estimates are inherently uncertain due to the difficulty of data collection and the geographic and temporal coverages of datasets 10 .Nonetheless, the FAO waste factors have been developed based on the best available knowledge.Uncertainties around food waste estimate are further explored in Coudard et al. 1 .

Harvested land, GHG emissions, and blue water from production of ACFW
We integrated the ACFW model with spatially explicit multi-regional input-output model from Sun et al. 11 to assess harvested land, GHG emissions, and blue water consumption of the different food commodities.The MRIO model uses the Food and Agriculture Biomass Input-Output dataset (FABIO) from Bruckner et al. 6 in physical units that relates the international final demand for food items with primary agricultural.The model select data for the year 2010 from FABIO.
FABIO covers 191 countries and 128 agricultural, food, and forestry products from 1986 to 2013.Data on food items are then combined and harmonized with the global dataset on consumer avoidable food waste from Coudard et al. that reports total quantities of consumer avoidable food waste for 192 countries.Since it is also based on the same nomenclature of FAOSTAT, the avoidable food waste items were readily matched to FABIO food items.As a result, avoidable food waste at the consumer level can be related back to the countries of their primary agricultural production.Bruckner et al. 6 provide further details on the construction of FABIO.
In the FABIO model, Brucker et al. acknowledge the potential issue of re-exports within IO and trade data.However, they propose a methodology to mitigate this concern.The model assumes that importers are more likely to accurately report the origin of traded commodities, as opposed to exporters determining the final destination.Import statistics are often more comprehensive due to customs' vested interest in accurate data collection for taxation purposes.
To address missing records, the FABIO model utilizes 'mirror' statistics from trade partners.This approach supplements incomplete data sets, ensuring a more comprehensive representation of trade flows.To verify that trade data reflects the final country of consumption, the FABIO model employs detailed trade statistics that track the origin and destination of goods.It distinguishes between intermediate and final goods, aligning trade flows with actual consumption locations.By tracing the movement of goods through international supply chains, from production to end-use, the FABIO model provides a precise of consumption-based demand for resources across different countries.
The harvested area used to grow the avoidable food waste is quantified using FAOSTAT crop and pasture area data, combined with SPAM, a spatial production allocation model 12 for 29 herbaceous crops and EarthStat 13 , a spatially explicit cropland and pastureland information dataset for the fodder crops.This step enables us to quantify the spatially explicit environmental impacts from the production of food commodities that will become ACFW.A GHG emissions dataset derived from FAOSTAT 4 at the national level is also linked to FABIO to quantify emissions from the agricultural activities that occur to produce the food items that ultimately become consumer avoidable food waste.The GHG emissions estimates, retrieved from Sun et al., were built using an older version of 100-year Global Warming Potentials (GWP), with those from the IPCC Fifth 5 Assessment Report (AR5) 14 with climate-carbon feedback (that is, 34 CO2e for CH4 and 298 66 CO2e for N2O).The same process is performed to quantify the blue water use during the agricultural production stage of the commodities, using datasets from the Water Footprint Network 15 for crop products and FAOSTAT for livestock products 16 .The calculated impacts of the ACFW are allocated to each production country based on the share of the sourcing country-mix of each consuming (and wasting) country.As such, if Country A sources 25% of its final consumption of beef products from Country B (the original producer), as a result 25% of the wasted harvested land used to produce the beef wasted in Country A is allocated to Country B.

Halving avoidable consumer food waste
We use the UN SDG 12.3.btarget as a basis for to model a reduction in avoidable food waste and to estimate the amounts of land that could potentially be restored to their PNV.The simplified approach halves ACFW (50% reduction) across all food categories in every country.
The avoidable food waste reduction scenario is of course highly idealized -as it is meant to explore the potential magnitude of such a shift on global natural resources and GHG emissions.The total environmental impacts (land use, blue water, and GHG emissions) that occurred during the production of ACFW are therefore halved.

GHG emissions reduction from the avoided ACFW End-of-Life
A GHG emissions dataset derived from FAOSTAT links the total quantities of food waste generated in each country with the total GHG emissions from the waste treatment activities (e.g., landfill), in tCO 2 eq.This dataset was developed by Crippa et al. 17 .This model determined the waste treatment activities based on data from the WhataWaste2.0dataset developed by the World Bank 18 .This dataset covers municipal waste from retail, food services and consumers, and therefore excludes industrial waste from agriculture and food processing.For each country, the total food waste reaching the municipal waste treatment activities is there defined as: Where:

S24
-    is the total amount of food waste reaching municipal waste treatment activities in the given country.- is the quantity of a food waste from food retail, reaching municipal waste treatment activities.- is the quantity of a avoidable consumer food waste reaching municipal waste treatment activities.- is the quantity of a unavoidable consumer food waste reaching municipal waste treatment activities.
We then isolate the mass share of ACFW relative the total food waste reaching municipal waste treatment, for each country.
ℎ = /    Eq. 9 Where: - ℎ is the share, in percentage mass, of avoidable consumer food waste relative to all food waste reaching municipal waste treatment activities.
We then allocate a share of the total emissions (retrieved from FAOSTAT) from municipal waste treatment for food waste for a given country to its ACFW.
=  ℎ *    Eq. 10 Where: -     is the GHG emissions associated with the waste treatment of ACFW in a given country -   is the total amount of GHG emissions (in tCO 2 eq) from the municipal waste treatment of food waste.
The potential GHG emissions reduction from halving ACFW in each country are then computed by halving the total emissions from ACFW of the country.While we computed the avoided production and end-of-life GHG emissions since we were particularly interested on the impacts of ACFW in places of production and waste, we did not include other sectors such as transportation, processing, wholesale, retail, hotel and restaurant food emissions.The environmental benefits, considering the full life-cycle of consumer food waste could therefore be expected to be larger than the figures presented in this study.

PNV and Carbon sequestration opportunities
Regarding the carbon sequestration benefits, we adopt Eq. 12 Where: - is the production of a specific crop or fodder item -ω is the dry-matter fraction of its harvested biomass, ℎ is its harvest index (fraction of total AGBC collected at harvest), - is the carbon-content fraction of its harvested dry mass - is the root-to-shoot ratio of the crop We determine the resulting carbon sequestration potential as the difference between the carbon stock of PNV and that of current use, using the work of Erb et al. 21for the AGBC and BGBC, and Sanderman et al. 22 , for the SOC.For the latter, we follow Sun et al.'s approach of a onetime 'committed' mass of carbon that is sequestered over an unspecified period after restoration is initiated (in practice on the order of 40-60 years).For further details on the carbon sequestration model, Sun et al., provided a detailed account of the methodologies, datasets, and assumption.Finally, we estimated the amounts of potential carbon sequestered due to sparing 50% of the land that were dedicated to produce ACFW.

Supplementary Sensitivity Analysis Results
Our study integrates multiple datasets and modelling approaches, introducing uncertainties in both data and modelling techniques.The estimation of the impacts of specific food commodities in individual nations should be interpreted cautiously due to uncertainties surrounding food waste data, trade flows, and environmental impact assessments.While we attempted to mitigate these uncertainties through rigorous data harmonization and sensitivity analyses, inherent limitations persist.
Specifically, regarding uncertainties surrounding food waste estimates, our study relies on global food waste estimates primarily derived from the Food and Agriculture Organization (FAO) report published in 2011.However, the temporal coverage and methodological variations in food waste studies pose challenges in quantifying precise uncertainty ranges 23 .For instance, a preliminary uncertainty analysis conducted by Coudard et al. 11 considered variations in food waste estimates over time, showing how estimates for specific commodities may fluctuate across different time intervals.
In this study, we conducted a broader sensitivity analysis on the data used in the ACFW dataset.UNEP Food Index annexes provided a list of all countries, each with a confidence label from High confidence to Very low confidence.Confidence ranges were then built following the report's suggestions -by attributing the confidence level to a specific confidence range for each country.The confidence range corresponding to each confidence level can be found below.
Table S17: Confidence level and confidence ranges adapted from UNEP Food Waste Index, 2022

Carbon sequestration potential
We estimated the amounts of potential carbon sequestered due to sparing the land that were dedicated to produce food that not eaten at the consumer stage following Sun et al.'s approach.Significant uncertainties, associated with biomass carbon estimates and soil organic carbon predictions remain in this aspect, underscoring the complexity of accurately estimating carbon sequestration potential.For these uncertainties, please refer to Sun et al. as they have addressed them through their own sensitivity analyses focusing on factors such as aboveground biomass carbon, belowground biomass carbon, and soil organic carbon (SOC).For biomass carbon, Sun et al. used the uncertainty dataset from Spawn et al. 37 , which was then used in the model framework to assess overall uncertainties.Future revisions to datasets, such as FABIO or SPAM, may also introduce additional uncertainties to our findings.Despite efforts to quantify and account for these uncertainties, ongoing advancements in data availability and modelling techniques may necessitate continual reassessment of our findings.Further uncertainty analysis is described in Sun et al. 11 .

Figure
Figure S2: a) ACFW harvested land impact origin.b) ACFW blue water impact origin.c) ACFW GHG emissions impact origin.These figures are the same as in main manuscript Fig1 a,b,c -in larger size.

Figure
Figure S3: a) Traded harvested land.b) Traded blue water.c) Traded GHG -of ACFW.These figures are the same as in main manuscript Fig2 a,b,c -in larger size.
Figure S4 -Commodity contribution to domestic ACFW land impacts in China

Figure S8 -
Figure S8 -Commodity contribution to domestic ACFW's GHG impacts in Brazil

Fig. S18 -
Fig. S18 -Sensitivity Analysis Delta Results for each country for the harvested land with the higher bound of the uncertainty range.

Fig. S19 -
Fig. S19 -Sensitivity Analysis Delta Results for each country for the production-based GHG emissions with the lower bound of the uncertainty range.

Fig. S20 -
Fig. S20 -Sensitivity Analysis Delta Results for each country for the production-based GHG emissions with the higher uncertainty range.

Fig. S21 -
Fig. S21 -Sensitivity Analysis Delta Results for each country for the blue water with the lower bound of the uncertainty range.

Fig. S22 -
Fig. S22 -Sensitivity Analysis Delta Results for each country for the blue water land with the higher uncertainty range.

Fig. S23 -
Fig. S23 -Sensitivity Analysis Delta Results for each country for the End-of-life based GHG emissions with the lower bound of the uncertainty range.

Fig. S24 -
Fig. S24 -Sensitivity Analysis Delta Results for each country for the End-of-life based GHG emissions with the higher bound of the uncertainty range.

Fig. S25 -
Fig. S25 -Sensitivity Analysis Delta Results for each country for the carbon sequestration potential with the lower bound of the uncertainty range.

Fig. S26 -
Fig. S26 -Sensitivity Analysis Delta Results for each country for the carbon sequestration potential with the higher uncertainty range.

Table S6 :
Top 5 contributing commodities to global blue water

Table S8 :
Top 5 AFW commodity-country pairs with the largest blue water offshored impacts.

Table S9 :
Top 5producing countries which commodities are wasted abroad as ACFW.TableS10and S11 presents the top countries and top commodity-country pairs for countries that both produce and waste their food domestically.

Table S10 :
Top 5 countries with the largest domestic blue water impacts.Country

Table S11 :
Top 5 AFW commodity-country pairs with the largest domestic blue water impacts of food produced and wasted domestically.Commodity

Table S12 :
Top 5 contributing commodities to global GHG emissions from the production of ACFW

Table S14 :
Top 5 AFW commodity-country pairs with the largest GHG emissions offshored impacts.

Table S15 :
Top 5producing countries which commodities are wasted abroad as ACFW.TableS16and S17 presents the top countries and top commodity-country pairs for countries that both produce and waste their food domestically.

Table S16 :
Top 5countries with the largest domestic blue water impacts.

Table S17 :
Top 5 AFW commodity-country pairs with the largest domestic GHG impacts of food produced and wasted domestically.
22n et al.'s approach (2022)where agricultural production is mapped using SPAM to spatially explicit cropland and pastureland, which we linked to the latest harmonized global AGBC and BGBC map (Spawn et al.19); a SOC stock map of the top 100 cm (De Sousa et al.20); and a PNV map with AGBC, BGBC and SOC (Erb et al.22; Sanderman et al. 201723).For both AGBC and BGBC, we allocated them into grid cells based on the spatial distribution of the 29 crops in SPAM and the fodder crop map in EarthStat. =  ( 0.451ℎ -1 + 1.025 -0.451)